Music Style Analysis Using the Random Forest Algorithm
DS 73-2 Proceedings of the 2nd International conference on Design Creativity Volume 2
Year: 2012
Editor: Duffy, A.; Nagai, Y.; Taura, T.
Author: Gómez de Silva Garza, A.; Herrera González, E.
Section: Design Creativity Methods and Integration
Page(s): 343-350
Abstract
This paper aims to discuss a method for autonomously analyzing a musical style based on the random forest learning algorithm. This algorithm needs to be shown both positive and negative examples of the concept one is trying to teach it. The algorithm uses the Hidden Markov Model (HMM) of each positive and negative piece of music to learn to distinguish the desired musical style from melodies that don‘t belong to it. The HMM is acquired from the coefficients that are generated by the Wavelet Transform of each piece of music. The output of the random forest algorithm codifies the solution space describing the desired style in an abstract and compact manner. This information can later be used for recognizing and/or generating melodies that fit within the analyzed style, a capability that can be of much use in computational models of design and computational creativity.
Keywords: style analysis, music style, markov model, wavelets, random forest